1.The
two kinds of error rates are pairwise, or per
comparison, error rates (PC) and familywise error
rates (FW).PC error rates are the
probability of making a Type I error on any given comparison.The probability of making a PC error rate is
your alpha (α) requirement (for multiple comparisons it would be the
corrected alpha for a particular test).FW error rates refer to the probability that a family (group-on the same
data) of conclusions will contain at least one Type I error.The FW error rate, if alpha is not corrected,
is very dependent on the number of tests being run (or, in other words,
included in a family).

FW: α=1-(1-α)c

(C=number
of comparisons.This formula really only
works for independent observations, but it is a decent estimate of FW when
observations are dependent.)

2.One
way to reduce the FW error rate would be to choose a select number of a priori
tests to submit your data to.However,
we often want to test a number of hypotheses, so the most common way to reduce
the FW error rate is to use a more conservative level of α for each
test.Such a correction is a Bonferroni correction whereby the alpha level is corrected
to be more conservative by dividing the desired alpha requirement by the number
of comparisons and then using the results for the new alpha requirement for a
given comparison (e.x., original α=.05 using 5
tests; .05=5=.01; so the new alpha level a statistic/test must meet to be
significant is .01).You also can reduce
the FW error rate by running each test at a stringent alpha level (e.x., α=.001) that is subjectively derived (rather
than the precisely calculated Bonferroni adjusted
α).

We adjust our alpha level for post hoc
testing because otherwise we could be capitalizing on chance.If we looked at our data and their means
and then picked one test to run, we would undoubtedly test those two
groups for which we expect the greatest likelihood of a significant
finding.However, in order to
decide the groups that are most likely to be significantly different, we
must have eyeballed the data and made cursory comparisons between all
groups.So although we only end up
running one test, we have actually done multiple tests with our eyes.Thus, our FW is not our alpha level, but
rather cα (the number of comparisons x
α).So, if we still hope to
retain our original α level (e.x.,
α=.05), we need to correct our alpha level.This is why we employ alpha (FW error
rate) corrections such as the Bonferronito
ensure that our results are still unlikely to be resulting from chance
(even though we have done multiple comparisons).

4.Yes,
we often adjust our alpha level for a priori tests.We do this, as was the case with post hoc
corrections, to minimize the FW error rate.So, anytime, we know we will be running multiple comparisons, we known
that our FW rate will be greater than our desired alpha unless we use a
corrected, more conservative alpha level for each test.Thus, anytime we run multiple comparisons a
priori we would determine the appropriate corrected α used to minimize FW
error concerns.

5.A
priori tests may always resemble post hoc tests in that the same statistical
procedures may be used for either type of tests.Additionally, a priori tests may resemble
post hoc tests when multiple a priori tests are chosen (as post hocs generally tend to be more numerous when
conducted).For example, if you decide a
priori to test all possible comparisons, there is little difference between the
a priori and all the possible post hoc tests that could be done (so they are
very similar in this instance).

6.FW: α=1-(1-.05)4

FW:
α=1-.81450625

FW:
α=.1855

The
approximate probability of committing a Type I (α) error (or, in other
words, the FW error rate) is .1855.